Image analysis is a process by which an imaging application determines a qualitative or quantitative feature based on input image data. For example, image analysis may be used determine whether an image is in or out of focus, if it is too bright or too dark, or how much sharpening should be applied to it. Image analysis may be implemented in conjunction with an image acquisition or image-printing device. In an image acquisition device it might be implemented for focus determination and visualization applications. In an image-printing device it might be implemented in the context of an image processing system.
Image processing is a process by which an imaging application alters input image data. For example, image processing may be used to change the color space of a digital image. Image processing may be implemented in conjunction with a printing device in order to adjust the color appearance or perceived sharpness of an image according to the specifications of the printing device. Current imaging applications receive image data from a wide array of diverse sources. For example, the image data may be a disposable camera image developed in an automatic film developing treatment machine, scanned at home, and compressed, or may be a manually enhanced high-resolution image from a professional digital image archive. A printer or any other imaging device is typically unable to operate in diverse conditions without first estimating the quality of its input images in order to process them accordingly.
Adaptive image processing is typically used to address problems associated with enhancing images having variable image quality (IQ). Currently, there are several methods of adaptive image processing with respect to image sharpness enhancement. However, the current available methods each present certain operational drawbacks or limitations.
Feature-based models are one category of current adaptive image sharpening methods. Feature-based models require the existence of identifiable features of the image. As such, feature-based models are only useful in situations where certain information is known about an image, and are not generally applicable to all images.
Model-based zero analysis methods are another category of adaptive image sharpening. However, model-based zero analysis assumes a particular parametric blur model and no image noise. Since a blur model for an image is often not known, this class of adaptive image sharpening is only useful in limited situations.
Autoregressive-moving average (ARMA) filter models can also be used for adaptive image sharpening. However, ARMA filter models are computationally intensive, requiring a substantial contribution of computing resources, and are occasionally unstable, thereby providing limited applicability. Similarly, single stage algorithms may be used for adaptive image processing, but are also computationally intensive.
Other current methods for adaptive image enhancement are problematic because they mix sharpness enhancement with contrast enhancement. Because an image can be sharp but have low contrast, or can be blurred but have a high contrast, it may be necessary to differentiate between sharpness and contrast problems, which these methods do no provide. Furthermore, other adaptive image enhancement methods assume a fractal image model, which is not true in the general case.